Optimising Neuroimaging Biomarkers for Dementia Using Deep Learning

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1) Brief description of the context of the research including potential impactDementia is a leading cause of global morbidity and mortality, yet successful treatments are scarce and mechanistic understanding is incomplete. Neuroimaging plays a key role in our understanding and treatment of dementia, and many biomarkers reflecting the brain's health have been proposed. However, these neuroimaging biomarkers are currently limited by poor generalisability, inadequate robustness to varying types and quality of data, slow computation speeds and arbitrary processing methods. Despite the many potential benefits of neuroimaging biomarkers, these limitations have hindered their use in clinical trials and clinical practice.2) Aims and Objectives This project aims to use emerging deep-learning techniques to overcome the above limitations. It will involve the analysis of magnetic resonance imaging (MRI) biomarkers, including whole-brain volume, hippocampal volume, ventricle size, cortical thickness, and white matter lesions. New image segmentation pipelines, based on deep learning, will be developed to generate these biomarkers. The goal is to demonstrate increased reliability, validity, and robustness of these biomarkers for use in dementia research and clinical trials.3) Novelty of Research Methodology The project involves developing novel neural-network methods, such as synthetic image augmentation on T1-weighted and T2-weighted MRI scans using generative networks, to enhance the robustness to varying types and quality of data. This research will also explore emerging network architectures, especially vision transformers which applies attention mechanism to differentially weighting the significance of each part of the neuroimaging data. These new image analysis pipelines will be validated using various local and public dementia MRI datasets to establish whether the resulting biomarkers increase sensitivity to disease effects, better predict disease progression and treatment response.4) Alignment to EPSRC's strategies and research areas EPSRC aims to "Transform Health and Healthcare" and develop "Artificial Intelligence (AI), Digitalisation and Data". Population ageing exposes more people to the risk of dementia. This project could help clinicians have a better understanding of dementia, allow patients to have a more timely and accurate diagnosis, and provide more supportive evidence in the development of dementia treatments. The project will explore different neural network architectures and methods in MRI analysis, which align well with two research areas in EPSRC - medical imaging and artificial intelligence, as part of the Healthcare Technologies theme.5) Any companies or collaborators involved None